Computational Chemistry, 7.5 credits

Contents

The course is divided into four modules:

Module 1 (1.5 credits): Quantum chemistry
The module comprises theory and a laboratory component. The theoretical part covers the fundamentals of electronic structure theory for molecules, including the Schrödinger equation and the Hamiltonian operator, orbital and MO concepts, and the central approximations and methods (Hartree–Fock and DFT), together with the choice of basis sets. The laboratory component will provide practical skills in performing and interpreting quantum chemical calculations.

Module 2 (2.0 credits): Molecular mechanics
The module covers the fundamentals of molecular mechanics (MM) and molecular dynamics (MD) as a complement to quantum chemical methods for studying larger molecular systems. The theoretical part introduces force fields and potential energy functions, including bonded and non-bonded interactions, partial charges and atom types, and methods for energy minimisation. The MD component covers statistical mechanics, thermostats and barostats, periodic boundary conditions, and time-integration algorithms. In the computer-based lab assignment, the students are introduced to free-energy calculations using molecular dynamics simulations by setting up, running energy minimization, equilibration, and production simulations, with subsequent data analysis.

Module 3 (2.0 credits): Bioinformatics
The module provides an introduction to structural bioinformatics, focusing on the construction and use of databases containing biological information, and on sequence and structure comparisons of proteins, including 3D structure comparisons and an introduction to the PDB format. The theory underlying the methods, their practical application, and the assessment of the quality of these comparisons are covered and discussed. Protein folding theory is covered at an overview level.

Module 4 (2.0 credits): QSAR: structure–activity relationships and predictive models
The module covers quantitative structure–activity relationships (QSAR) as an example of data-driven, chemometric modelling in chemistry, in which the physical and chemical properties of molecules are related to biological effects. Students calculate molecular descriptors and apply multivariate statistical methods to build and validate predictive models of biological activity, with emphasis on the selection and interpretation of descriptors and on model validation.

Expected learning outcomes

Intended Learning Outcomes

Knowledge and understanding
On completion of the course, the student shall be able to:

• Describe fundamental concepts in quantum chemistry, molecular mechanics, and molecular dynamics, and explain what distinguishes key methods and approximations from one another.
• Describe fundamental methods in structural bioinformatics, including sequence comparison, comparative modeling, and protein folding.
• Explain how data-driven methods, such as machine-learned force fields and AI-based structure prediction, relate to physics-based computational methods with respect to accuracy, computational cost, and domain of validity.
• Describe theoretical methods, and plan and perform computer-based calculations of physical and chemical properties of molecules, relating these to biological/environmental effects using chemometric computational models.

Skills and abilities
On completion of the course, the student shall be able to:

• Set up, perform, and interpret quantum chemical calculations and molecular dynamics simulations via remote access to a computing cluster.
• Build and validate predictive QSAR models and interpret the results in chemical terms.
• Use browser-based tools for cheminformatics and machine learning to explore chemical datasets and to build and evaluate predictive models, and critically review scientific publications in AI and chemistry.

Judgement and approach
On completion of the course, the student shall be able to:

• Critically assess the plausibility, limitations, and generalizability of computational results and predictive models, including how choice of method and data quality affect accuracy and domain of validity.
• Reflect on research-ethical aspects of artificial intelligence in chemical research, including the responsible and transparent use of AI tools in one's own work.
• Analyze, critically review, and present results from computer-based computational chemistry.

Required Knowledge

90 ECTS credits, of which at least 45 ECTS credits are in the main area of chemistry including the course in Chemometrics 7,5 ECTS credits (5KE053), or the equivalent and English 6/level 2.

Form of instruction

Teaching consists of:

• Lectures and seminars: an introduction to theoretical concepts and principles, together with critical review of the scientific literature.
• Self-study based on the specified course material and literature.
• Laboratory work and project work: Practical calculations are carried out in browser-based environments, using a computing cluster for quantum chemical calculations and molecular dynamics simulations, as well as browser-based software for cheminformatics and data analysis.

Examination modes

The course is examined through the following tests:

Modules 1–3 are assessed in terms of theoretical concepts through an individual written examination, which is graded as either Fail (U), Pass (G) or Pass with distinction (VG).

Practical projects from Modules 1–3 are assessed individually via one written laboratory report for each module. Each laboratory report is graded as either Fail (U) or Pass (G).

Module 4 is assessed through a written project report, which is graded as either Fail (U) or Pass (G), and an oral presentation, which is graded as either Fail (U) or Pass (G).

To pass the course, students must pass all modules. The final grade on the course (U/G/VG) is based on an equal weighting of the grades from Modules 1–3.

A student who has passed an examination may not take the same examination again with the aim of achieving a higher grade. A student who has failed a course or part of a course twice, has the right to make a written request to have another examiner appointed, unless special reasons speak against it (HF Chapter 6, Section 22). Requests for a different examiner are to be made to the Head of the Department of Chemistry.

Other regulations

Adapted assessment: For students who have a decision on recommended support due to a disability, the examiner may decide on deviations from the examination format specified in the syllabus. Individual adaptation of the examination format shall be considered based on the student’s needs and the intended learning outcomes of the course. For more information, see the Procedures for support to students with disabilities and the Regulations for grading and examination.

Transfer of credits: Applications for credit transfer are assessed on an individual basis. This course may not be included in a degree alongside another course with similar content. If in doubt, students should consult the study counsellor for the relevant course. Applications for credit transfer should be submitted to the Student Services Office/Examina. Further information on credit transfer is available on Umeå University’s student website and in the Higher Education Ordinance (Chapter 6). A rejection of an application for credit transfer may be appealed (Higher Education Ordinance, Chapter 12) to the Higher Education Appeals Board. This applies whether the entire application or parts of it are rejected.

Transitional provisions

In the event that the syllabus ceases to apply or undergoes major changes, students are guaranteed at least three examinations (including the regular examination opportunity) according to the regulations in the syllabus that the student was originally registered on for a period of a maximum of two years from the time that the previous syllabus ceased to apply or that the course ended.

Rules for grades and examination govern what applies regarding retakes when a course has been discontinued or a syllabus has undergone major changes.